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10.Artificial Neuron - Basic Elements
Neuron consists of three basic components - weights,
thresholds, and a single activation function
In practice, neurons generally do not fire (produce an output) unless their
total input goes above a threshold value.
Activation Functions
An activation function f performs a mathematical operation on the signal
output. The activation functions are chosen depending upon the type of
problem to be solved by the network. The most common activation
functions are:
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Activation functions are called bipolar continuous and bipolar binary
functions, respectively. The word "bipolar" is used to point out that both
positive and negative responses of neurons are produced for this
definition of the activation function.
Activation functions are called unipolar continuous and unipolar binary
functions, respectively.
11.NEURAL NETWORK LEARNING RULES
Our focus in this section will be artificial neural network learning rules.
A neuron is considered to be an adaptive element. Its weights are
modifiable depending on the input signal it receives, its output value, and
the associated teacher response. In some cases the teacher signal is not
available and no error information can be used, thus the neuron will
modify its weights based only on the input and/or output. This is the case
for unsupervised learning. Let us study the learning of the weight vector
wi, or its components wy connecting the j7th input with the i'th neuron..
In general, the j'th input can be an output of another neuron or it can be an
external input. Our discussion in this section will cover single-neuron and
single-layer network supervised learning and simple cases of
unsupervised learning. Under different learning rules, the form of the
neuron's activation function may be different. Note
that the threshold parameter may be included in learning as one of the
weights. This would require fixing one of the inputs, say x,. We will
assume here that x,, if fixed, takes the value of - 1.
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